{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-28T11:23:20.527270Z",
     "start_time": "2018-08-28T11:23:20.519750Z"
    }
   },
   "outputs": [],
   "source": [
    "import random as rn\n",
    "import os\n",
    "import numpy as np\n",
    "os.environ['PYTHONHASHSEED'] = '0'\n",
    "np.random.seed(42)\n",
    "rn.seed(123)\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.model_selection import PredefinedSplit\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import uniform\n",
    "from scipy.stats import randint as sp_randint\n",
    "from sklearn.metrics import make_scorer\n",
    "pd.set_option('display.max_columns', 50)\n",
    "pd.set_option('display.max_rows', 100)\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "\n",
    "## 1.1 定义一下路径\n",
    "Path = 'D:\\\\APViaML'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-28T11:23:20.533789Z",
     "start_time": "2018-08-28T11:23:20.529777Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def get_demo_dict_data():\n",
    "    file = open(Path + '\\\\data\\\\alldata_demo_top1000.pkl','rb')\n",
    "    raw_data = pickle.load(file)\n",
    "    file.close()\n",
    "    return raw_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-28T11:23:20.543822Z",
     "start_time": "2018-08-28T11:23:20.535799Z"
    },
    "code_folding": [
     0,
     12
    ]
   },
   "outputs": [],
   "source": [
    "def Evaluation_fun(predict_array,real_array):\n",
    "    List1 = []\n",
    "    List2 = []\n",
    "    if len(predict_array) != len(real_array):\n",
    "        print('Something is worng!')\n",
    "    else:\n",
    "        for i in range(len(predict_array)):\n",
    "            List1.append(np.square(predict_array[i]-real_array[i]))\n",
    "            List2.append(np.square(real_array[i]))\n",
    "        result = round(100*(1 - sum(List1)/sum(List2)),3)\n",
    "    return result\n",
    "\n",
    "def my_custom_score_func(ground_truth, predictions):\n",
    "    \n",
    "    num1 =sum((ground_truth-predictions)**2)\n",
    "    num2 = sum(ground_truth**2)\n",
    "    return 1-num1/num2\n",
    "\n",
    "self_score = make_scorer(my_custom_score_func, greater_is_better=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-28T11:23:20.563873Z",
     "start_time": "2018-08-28T11:23:20.545826Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def rolling_model_ENet_annual(\n",
    "    df_X ,\n",
    "    df_Y ,\n",
    "    num1 = 200):\n",
    "                                    \n",
    "    y_predict_list = []\n",
    "    test_performance_score_list = []\n",
    "    coef_df = pd.DataFrame()\n",
    "\n",
    "    for i in range(30):\n",
    "        print(i)\n",
    "        ## define data index\n",
    "        traindata_startyear_str = str(1958) \n",
    "        traindata_endyear_str = str(i + 1987) \n",
    "        vdata_startyear_str = str(i + 1976) \n",
    "        vdata_endyear_str = str(i + 1987) \n",
    "        testdata_startyear_str = str(i + 1988) \n",
    "\n",
    "        ## get data     \n",
    "        X_traindata =  np.array(df_X.loc[traindata_startyear_str:traindata_endyear_str])\n",
    "        Y_traindata = np.array(df_Y.loc[traindata_startyear_str:traindata_endyear_str])\n",
    "        X_vdata = np.array(df_X.loc[vdata_startyear_str:vdata_endyear_str])\n",
    "        Y_vdata = np.array(df_Y.loc[vdata_startyear_str:vdata_endyear_str])\n",
    "        X_testdata = np.array(df_X.loc[testdata_startyear_str])\n",
    "        Y_testdata = np.array(df_Y.loc[testdata_startyear_str])\n",
    "\n",
    "\n",
    "        num_valid_size = len(X_traindata)-len(X_vdata)\n",
    "        \n",
    "        test_fold = -1 * np.ones(len(X_traindata))\n",
    "        test_fold[num_valid_size:] = 0\n",
    "        ps = PredefinedSplit(test_fold)\n",
    "        \n",
    "        \n",
    "        # specify parameters and distributions to sample from\n",
    "        param_dist = {'alpha': uniform(0.00001, 0.1),\n",
    "                      'power_t':uniform(0.1, 0.9),\n",
    "                      'l1_ratio':uniform(0.1, 0.9),\n",
    "                     'eta0':uniform(0.00001, 0.1),\n",
    "                     'epsilon':uniform(0.01, 0.9),\n",
    "                     'max_iter':sp_randint(5, 10000),\n",
    "                     'tol':[0.01,0.001,0.0001,0.00001],\n",
    "                     'fit_intercept':[True,False]}\n",
    "        \n",
    "        clf = SGDRegressor(shuffle =False,\n",
    "                 loss = 'huber',\n",
    "                 penalty=  'elasticnet',random_state=100)\n",
    "        \n",
    "        # run randomized search\n",
    "        n_iter_search = num1\n",
    "        estim = RandomizedSearchCV(clf, param_distributions=param_dist,\n",
    "                                           n_iter=n_iter_search,scoring=self_score,\n",
    "                                          cv=ps.split(),iid=False,\n",
    "                                          random_state=100)\n",
    "                        \n",
    "        estim.fit(X_traindata, Y_traindata)\n",
    "\n",
    "        best_estimator = estim.best_estimator_\n",
    "        estim = best_estimator.fit(X_traindata[:num_valid_size], Y_traindata[:num_valid_size])     \n",
    "\n",
    "        file = open(Path + '\\\\model\\\\ENet\\\\' + testdata_startyear_str+ 'Model_ENet_Top1000_Prediction.pkl', 'wb')\n",
    "        pickle.dump(estim, file)\n",
    "        file.close()\n",
    "        \n",
    "        ## model testing\n",
    "         \n",
    "        test_pre_y_arry = estim.predict(X_testdata)\n",
    "        y_predict_list1=[]\n",
    "        for x in test_pre_y_arry:\n",
    "            y_predict_list.append(x)\n",
    "            y_predict_list1.append(x)        \n",
    "        test_performance_score =  Evaluation_fun(y_predict_list1,Y_testdata )\n",
    "        test_performance_score_list.append(test_performance_score)\n",
    "        \n",
    "        ## get the coef\n",
    "        coef_array = estim.coef_\n",
    "        coef_df[testdata_startyear_str] = coef_array\n",
    "        \n",
    "    return y_predict_list,test_performance_score_list,coef_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-28T11:23:21.580084Z",
     "start_time": "2018-08-28T11:23:20.565879Z"
    }
   },
   "outputs": [],
   "source": [
    "data = get_demo_dict_data()\n",
    "top_1000_data_X = data['X']\n",
    "top_1000_data_Y = data['Y']\n",
    "del data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-28T14:05:03.904739Z",
     "start_time": "2018-08-28T11:23:21.583091Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
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      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n"
     ]
    }
   ],
   "source": [
    "y_predict_list,test_performance_score_list,coef_df1 = rolling_model_ENet_annual(df_X = top_1000_data_X ,df_Y = top_1000_data_Y,num1=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-28T14:25:50.023944Z",
     "start_time": "2018-08-28T14:25:50.018932Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.498766666666667\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(test_performance_score_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-28T14:25:54.282014Z",
     "start_time": "2018-08-28T14:25:54.122934Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.664"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_real = np.array(top_1000_data_Y.loc['1988':])\n",
    "Evaluation_fun(y_predict_list,y_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T01:10:16.233532Z",
     "start_time": "2018-08-29T01:10:16.130251Z"
    }
   },
   "outputs": [],
   "source": [
    "file = open(Path + '\\\\output\\\\data\\\\Model_ENet_Top1000_Prediction.pkl', 'wb')\n",
    "pickle.dump(y_predict_list, file)\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T01:11:21.985554Z",
     "start_time": "2018-08-29T01:11:21.142307Z"
    }
   },
   "outputs": [],
   "source": [
    "coef_df1.to_excel(Path +'\\\\output\\\\ENet_coef_list.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T01:11:23.284993Z",
     "start_time": "2018-08-29T01:11:23.277975Z"
    }
   },
   "outputs": [],
   "source": [
    "coef_df1.replace(to_replace=0,value=np.nan,inplace=True)\n",
    "temp = coef_df1.notna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T01:11:24.619311Z",
     "start_time": "2018-08-29T01:11:24.613295Z"
    }
   },
   "outputs": [],
   "source": [
    "coef_df2 = np.array(temp.iloc[:94])\n",
    "\n",
    "for i in range(8):\n",
    "    j = (i+1)*94\n",
    "    k = (i+2)*94\n",
    "    coef_df2 =  coef_df2 +  np.array(temp.iloc[j:k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T01:11:25.482397Z",
     "start_time": "2018-08-29T01:11:25.478389Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(coef_df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T01:11:25.994301Z",
     "start_time": "2018-08-29T01:11:25.990290Z"
    }
   },
   "outputs": [],
   "source": [
    "coef_final = pd.DataFrame(coef_df2,index=range(94),columns=coef_df1.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T01:11:26.785820Z",
     "start_time": "2018-08-29T01:11:26.781811Z"
    }
   },
   "outputs": [],
   "source": [
    "def count_zero(df):\n",
    "    num_char_Enet = []\n",
    "    for i in range(len(df.columns)):\n",
    "        num=0\n",
    "        for j in range(len(df)):\n",
    "            if df.iloc[j,i]==True:\n",
    "                num = num+1\n",
    "        num_char_Enet.append(num)\n",
    "    return num_char_Enet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T01:11:27.576922Z",
     "start_time": "2018-08-29T01:11:27.525779Z"
    }
   },
   "outputs": [],
   "source": [
    "num_char_Enet = count_zero(df=coef_final )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T01:11:28.739329Z",
     "start_time": "2018-08-29T01:11:28.734311Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[18,\n",
       " 0,\n",
       " 22,\n",
       " 19,\n",
       " 32,\n",
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       " 5]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_char_Enet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T01:11:45.348273Z",
     "start_time": "2018-08-29T01:11:45.342256Z"
    }
   },
   "outputs": [],
   "source": [
    "def figure_char(a):\n",
    "    plt.subplots(figsize=(12,6))\n",
    "    plt.rcParams['font.sans-serif'] = 'simhei'\n",
    "    plt.rcParams['axes.unicode_minus'] = False\n",
    "    plt.plot(range(1987,2017),a,\n",
    "             linestyle = '-', # 折线类型\n",
    "             linewidth = 2, # 折线宽度\n",
    "             color = 'indianred', # 折线颜色\n",
    "             marker = 'o', # 点的形状\n",
    "             markersize = 6, # 点的大小\n",
    "             markeredgecolor='black', # 点的边框色\n",
    "             markerfacecolor='indianred') \n",
    "    plt.ylabel('# of Comp.')  \n",
    "    plt.title('Enet+H')\n",
    "    plt.grid(True,ls='--')\n",
    "    ax = plt.gca()\n",
    "    ax.spines['top'].set_visible(False) #去掉上边框\n",
    "    ax.spines['right'].set_visible(False) #去掉右边框\n",
    "    plt.savefig(Path + '\\\\output\\\\fig3_Enet.jpg',bbox_inches = 'tight')\n",
    "     # 显示图形\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T01:11:46.223722Z",
     "start_time": "2018-08-29T01:11:45.838612Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_char(num_char_Enet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "toc": {
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